Video-Based Big Data Analytics in Cyberlearning

Autor/inn/en	Wang, Shuangbao; Kelly, William
Titel	Video-Based Big Data Analytics in Cyberlearning
Quelle	In: Journal of Learning Analytics, 4 (2017) 2, S.36-46 (11 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1929-7750
Schlagwörter	Video Technology; Online Courses; Educational Technology; Information Security; Computer Security; Program Descriptions; Computer Science Education; Peer Relationship; Teacher Student Relationship; Data Analysis; Computer Software; Search Strategies; Recordkeeping; Cooperative Learning; Second Languages; Computer Assisted Testing; Masters Degrees; Graduate Students; Outcomes of Education; Maryland + Suchen Sie Ihr Suchwort? Online course; Online-Kurs; Unterrichtsmedien; Computervirus; Computersicherheit; Computer science lessons; Informatikunterricht; Peer-Beziehungen; Teacher student relationships; Lehrer-Schüler-Beziehung; Auswertung; Suchstrategie; Leistungsnachweis; Kooperatives Lernen; Second language; Zweitsprache; Graduate Study; Student; Students; Aufbaustudium; Graduiertenstudium; Hauptstudium; Studentin; Lernleistung; Schulerfolg
Abstract	In this paper, we present a novel system, inVideo, for video data analytics, and its use in transforming linear videos into interactive learning objects. InVideo is able to analyze video content automatically without the need for initial viewing by a human. Using a highly efficient video indexing engine we developed, the system is able to analyze both language and video frames. The time-stamped commenting and tagging features make it an effective tool for increasing interactions between students and online learning systems. Our research shows that inVideo presents an efficient tool for learning technology research and increasing interactions in an online learning environment. Data from a cybersecurity program at the University of Maryland show that using inVideo as an adaptive assessment tool, interactions between student-student and student-faculty in online classrooms increased significantly across 24 sections program-wide. (As Provided).
Anmerkungen	Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: http://learning-analytics.info/journals/index.php/JLA/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01